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Simultaneous Distribution Control Simultaneous Distribution Control and Privacy Protection for Proxy and Privacy Protection for Proxy

based Media Distributionbased Media Distribution

Songqing Chen (George Mason UniversityGeorge Mason University)

Shiping Chen (George Mason UniversityGeorge Mason University)

Huiping Guo (California State UniversityCalifornia State University)

Bo Shen (Hewlett-Packard LabsHewlett-Packard Labs)

Sushil Jajodia (George Mason UniversityGeorge Mason University)

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Background

• Compared to Web content delivery, Internet media distribution is challenging:– Large object size– Continuous demand of network, disk bandwidth

• Lots of proxy-based solutions: – Silo, partial sequence caching, layered caching,

scabale proxy caching, QBIX, prefix, segment caching, video staging…… good performance

Any of these ideas is practically/widely deployed?

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Lack Distribution Control

Server Proxy Client

I cannot get pay for these accesses!

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Existing Solutions – for distribution control

• Common practice (Does not work with proxy caching)– Pay-per-view/membership– DRM (Digital Right Management)

• Proxy-based solutions– Hardware-assisted encryption/decryption

(special device requirement)– RSA-based multi-key (vulnerable to client collusion)

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Lack Sufficient Privacy Protection

• Current practice could endanger your private information– WWW (when & what & where) – Your preferences, payment methods

• e.g., what kinds of movies you are always interested in?

– ……– May be used for uninvited ads or investigation

Little is considered in existing media distribution solutions

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Conflicting Interests

• Privacy Protection (end-user’s interests)– Proxy has good potential for privacy protection

• Distribution control (content provider’s interests)– Only legitimate users could be granted access – Normally requires user’s identity

Can we simultaneously achieve both goals for two partieswhile proxy caching can be leveraged?

Conflicting

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Our Contributions

• Provide a framework to achieve simultaneous distribution control and privacy protection– El Gamal based scheme for distribution control – Shamir-Omura based scheme for privacy protection

• Propose and evaluate the algorithm in cooperative proxy environments– Considering traffic amortization and proactive

replacement

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Outline

• Simultaneous Distribution Control and Privacy Protection– Distribution Control Principle– Privacy Protection Principle

• Algorithm Design and Evaluation

• Conclusions

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Key Division Cipher

• M = D(E(M, Ke) , Kd)

• Kd = Kd1 Kd2

• M = D(D(E(M, Ke), Kd1), Kd2)

• El Gamal is a key division cipher system on “+”.

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Distribution Control

Client Proxy Server

XB = XB1 +XB2

XB < qYB = αXB mod q

Random k <qK = (YB)k (mod q)C1= αk (mod q)C2= KM (mod q)

K1 = (C1)XB1 mod qM2 = C2 / K1 mod q

C2

K2 = (C1)XB2 mod qM = M2 / K2 mod q

M2

(C1, XB1)(C1, XB2)

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Commutative Cipher

• For any two keys: Ke1 and Ke2

• E(E(M, Ke1), Ke2) = E(E(M, Ke2), Ke1)

• Shamir-Omura has commutative property.

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Privacy Protection

Client Proxy Server (KE, KD)IDS= E(ID, KE)

(IDS, Movie) (Ke, Kd)IDC= E(ID, Ke)

E(IDC , KE)= E(E(ID, Ke), KE)= (IDC)S

D((IDC)S, Kd)= D(E(E(ID, Ke), KE), Kd)= E(ID, KE)= IDS

IDS

IDC

(IDC)S

IDS

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Our Unified SchemeAssumptions

• k anonymity– The server only knows a client is accessing one of k objects

• Objects are classified into n classes (e.g., price), each with more than k objects

• Privacy protection (Shamir-Omura)– Each object can only be identified via its encrypted ID on the proxy – Encryption key KE for IDs is same for objects in the same class

• Distribution control (El Gamal)– Each object is encrypted with a different key – Encryption key is divided into two parts, e.g., E(M, SC+Si)

• SC is common for the class• Si is different for each object

– Si is encrypted with KE

– ID and E(Si, KE) are available for client access

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client proxy server(ID, E(Si,KE)) list

Want to access some movie: ID

(E(ID, KE), E(M, SC+Si))

E(ID, Ke) || E(E(Si, KE), Ke)1. Get payment;2. E(E(ID, Ke), KE); 3. D(E(E(Si, KE), Ke), KD)=E(Si, Ke);4.SC = SC1+SC2

E(E(ID, Ke), KE) || E(Si, Ke) || SC2

SC11. D(E(Si, Ke), Kd)= Si

2. D(E(E(ID, Ke), KE), Kd)=E(ID, KE) = IDS

IDS

D(E(M, SC+Si), SC1)

D(E(M, SC+Si), SC1)

D(D(E(M, SC+Si), SC1), SC2+Si)

Objects are pre-cached in

the proxy!

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Brief Analysis

• Proxy and clients do not collude – enable distribution control

• Proxy and servers do not collude – provide privacy protection

• For each access to the server, instead of fetching 1 object, (k-1) additional objects must be fetched for privacy protection – additional traffic – can we utilize?

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Outline

• Simultaneous Distribution Control and Privacy Protection

• Algorithm Design and Evaluation

• Conclusions

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Design Space

• Work independently or cooperatively?– Cost-Amortized Request Admission

• Which (K-1) objects to fetch?– Aggressive Object Selection

• Which objects to replace?– Proactive Replacement

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Cost-amortized Request Admission

• Requested object is not in local or peer cache– Counting how many (r) requests from how

many (p) proxies to access server at this time

– Each proxy fetches additional objects

p

rk

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Aggressive Object Selection

• After determining the number of additional objects to fetch:– In the first phase, select objects according to

the object popularity

– In the second phase, select objects according to the object size

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Proactive Replacement

• Always use popularity based replacement to make room for the requested object

• For additionally fetched objects:– In the first phase, using popularity based replacement

to cache the additionally fetched objects

– In the second phase, the additionally fetched objects are discarded

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Evaluation

• Trace driven simulation – using a synthetic workload based on a server log

through duplication– Total unique objects: 934– Total unique object size: 67 GB– Total number of requests: 64227– Object size: 288 KB to 638 MB – Average traffic per request: 222 MB– Number of cooperative proxies: 4– Number of object classes: 5– Privacy level k: 4

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Evaluated Strategies

Privacy

Protection

Pro-active

Replacement

Amortizing

Cost

base No No No

strategy1 Yes No No

strategy2 Yes Yes No

strategy3 Yes Yes Yes

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Cache Size-- Additional Traffic

1% of the total client accessed traffic

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Cache Size-- Local Hit Ratio & Peer Hit Ratio

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Cache Size-- Local Byte Hit Ratio & Peer Byte Hit Ratio

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Outline

• Simultaneous Distribution Control and Privacy Protection

• Algorithm Design and Evaluation

• Conclusions

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Conclusion

• Extended El Gamal for distribution control and Shamir-Omura for privacy protection

• Proposed a unified algorithm to achieve them simultaneously

• Proposed an algorithm and evaluated in a cooperative proxy environment

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Questions?

Thanks to anonymous reviewers, Bill Bynum (William and Mary), Xiaodong Zhang (Ohio State University).